
import coval  # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator

import datasets


lowerCamelCase_    :			int    =	datasets.logging.get_logger(__name__)


lowerCamelCase_    :			Any    =	"""\
@InProceedings{moosavi2019minimum,
    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},
    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},
    year = {2019},
    booktitle = {Proceedings of the 57th Annual Meeting of
        the Association for Computational Linguistics (Volume 1: Long Papers)},
    publisher = {Association for Computational Linguistics},
    address = {Florence, Italy},
}

@inproceedings{10.3115/1072399.1072405,
author = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},
title = {A Model-Theoretic Coreference Scoring Scheme},
year = {1995},
isbn = {1558604022},
publisher = {Association for Computational Linguistics},
address = {USA},
url = {https://doi.org/10.3115/1072399.1072405},
doi = {10.3115/1072399.1072405},
booktitle = {Proceedings of the 6th Conference on Message Understanding},
pages = {45–52},
numpages = {8},
location = {Columbia, Maryland},
series = {MUC6 ’95}
}

@INPROCEEDINGS{Bagga98algorithmsfor,
    author = {Amit Bagga and Breck Baldwin},
    title = {Algorithms for Scoring Coreference Chains},
    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},
    year = {1998},
    pages = {563--566}
}

@INPROCEEDINGS{Luo05oncoreference,
    author = {Xiaoqiang Luo},
    title = {On coreference resolution performance metrics},
    booktitle = {In Proc. of HLT/EMNLP},
    year = {2005},
    pages = {25--32},
    publisher = {URL}
}

@inproceedings{moosavi-strube-2016-coreference,
    title = \"Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric\",
    author = \"Moosavi, Nafise Sadat  and
      Strube, Michael\",
    booktitle = \"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",
    month = aug,
    year = \"2016\",
    address = \"Berlin, Germany\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/P16-1060\",
    doi = \"10.18653/v1/P16-1060\",
    pages = \"632--642\",
}

"""

lowerCamelCase_    :			Optional[Any]    =	"""\
CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which
implements of the common evaluation metrics including MUC [Vilain et al, 1995],
B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],
LEA [Moosavi and Strube, 2016] and the averaged CoNLL score
(the average of the F1 values of MUC, B-cubed and CEAFe)
[Denis and Baldridge, 2009a; Pradhan et al., 2011].

This wrapper of CoVal currently only work with CoNLL line format:
The CoNLL format has one word per line with all the annotation for this word in column separated by spaces:
Column	Type	Description
1	Document ID	This is a variation on the document filename
2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
3	Word number
4	Word itself	This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.
5	Part-of-Speech
6	Parse bit	This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the \"([pos] [word])\" string (or leaf) and concatenating the items in the rows of that column.
7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a \"-\"
8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.
9	Word sense	This is the word sense of the word in Column 3.
10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.
11	Named Entities	These columns identifies the spans representing various named entities.
12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.
N	Coreference	Coreference chain information encoded in a parenthesis structure.
More informations on the format can be found here (section \"*_conll File Format\"): http://www.conll.cemantix.org/2012/data.html

Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md

CoVal code was written by @ns-moosavi.
Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py
The test suite is taken from https://github.com/conll/reference-coreference-scorers/
Mention evaluation and the test suite are added by @andreasvc.
Parsing CoNLL files is developed by Leo Born.
"""

lowerCamelCase_    :			Dict    =	"""
Calculates coreference evaluation metrics.
Args:
    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.
        Each prediction is a word with its annotations as a string made of columns joined with spaces.
        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
        See the details on the format in the description of the metric.
    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.
        Each reference is a word with its annotations as a string made of columns joined with spaces.
        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
        See the details on the format in the description of the metric.
    keep_singletons: After extracting all mentions of key or system files,
        mentions whose corresponding coreference chain is of size one,
        are considered as singletons. The default evaluation mode will include
        singletons in evaluations if they are included in the key or the system files.
        By setting 'keep_singletons=False', all singletons in the key and system files
        will be excluded from the evaluation.
    NP_only: Most of the recent coreference resolvers only resolve NP mentions and
        leave out the resolution of VPs. By setting the 'NP_only' option, the scorer will only evaluate the resolution of NPs.
    min_span: By setting 'min_span', the scorer reports the results based on automatically detected minimum spans.
        Minimum spans are determined using the MINA algorithm.

Returns:
    'mentions': mentions
    'muc': MUC metric [Vilain et al, 1995]
    'bcub': B-cubed [Bagga and Baldwin, 1998]
    'ceafe': CEAFe [Luo et al., 2005]
    'lea': LEA [Moosavi and Strube, 2016]
    'conll_score': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)

Examples:

    >>> coval = datasets.load_metric('coval')
    >>> words = ['bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -',
    ... 'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)',
    ... 'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)',
    ... 'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -',
    ... 'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -',
    ... 'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -']
    >>> references = [words]
    >>> predictions = [words]
    >>> results = coval.compute(predictions=predictions, references=references)
    >>> print(results) # doctest:+ELLIPSIS
    {'mentions/recall': 1.0,[...] 'conll_score': 100.0}
"""







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase=False  ,     lowerCamelCase=False  ,     lowerCamelCase=True  ,     lowerCamelCase=False  ,     lowerCamelCase="dummy_doc"    )	->						Any:
				UpperCamelCase_:				List[Any]						=    {doc: key_lines}
				UpperCamelCase_:				int						=    {doc: sys_lines}

				UpperCamelCase_:				str						=    {}

				UpperCamelCase_:				List[Any]						=    0
				UpperCamelCase_:				Optional[Any]						=    0
				UpperCamelCase_:				int						=    0
				UpperCamelCase_:				Optional[int]						=    0
				UpperCamelCase_:				Optional[int]						=    0
				UpperCamelCase_:				Optional[int]						=    0

				UpperCamelCase_, UpperCamelCase_:				Tuple						=    reader.get_doc_mentions(lowerCamelCase  ,     key_doc_lines[doc]  ,     lowerCamelCase    )
				key_singletons_num += singletons_num

				if NP_only or min_span:
								UpperCamelCase_:				Optional[int]						=    reader.set_annotated_parse_trees(lowerCamelCase  ,     key_doc_lines[doc]  ,     lowerCamelCase  ,     lowerCamelCase    )

				UpperCamelCase_, UpperCamelCase_:				List[str]						=    reader.get_doc_mentions(lowerCamelCase  ,     sys_doc_lines[doc]  ,     lowerCamelCase    )
				sys_singletons_num += singletons_num

				if NP_only or min_span:
								UpperCamelCase_:				int						=    reader.set_annotated_parse_trees(lowerCamelCase  ,     key_doc_lines[doc]  ,     lowerCamelCase  ,     lowerCamelCase    )

				if remove_nested:
								UpperCamelCase_, UpperCamelCase_:				Any						=    reader.remove_nested_coref_mentions(lowerCamelCase  ,     lowerCamelCase    )
								key_nested_coref_num += nested_mentions
								key_removed_nested_clusters += removed_clusters

								UpperCamelCase_, UpperCamelCase_:				List[Any]						=    reader.remove_nested_coref_mentions(lowerCamelCase  ,     lowerCamelCase    )
								sys_nested_coref_num += nested_mentions
								sys_removed_nested_clusters += removed_clusters

				UpperCamelCase_:				Tuple						=    reader.get_mention_assignments(lowerCamelCase  ,     lowerCamelCase    )
				UpperCamelCase_:				Any						=    reader.get_mention_assignments(lowerCamelCase  ,     lowerCamelCase    )

				UpperCamelCase_:				List[Any]						=    (key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)

				if remove_nested:
								logger.info(
								    """Number of removed nested coreferring mentions in the key """
								    F'''annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}'''    )
								logger.info(
								    """Number of resulting singleton clusters in the key """
								    F'''annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}'''    )

				if not keep_singletons:
								logger.info(
								    F'''{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system '''
								    """files, respectively"""    )

				return doc_coref_infos







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						Union[str, Any]:
				UpperCamelCase_:				Dict						=    get_coref_infos(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )

				UpperCamelCase_:				Optional[int]						=    {}
				UpperCamelCase_:				List[Any]						=    0
				UpperCamelCase_:				Any						=    0

				for name, metric in metrics:
								UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Optional[Any]						=    evaluator.evaluate_documents(lowerCamelCase  ,     lowerCamelCase  ,     beta=1    )
								if name in ["muc", "bcub", "ceafe"]:
												conll += fa
												conll_subparts_num += 1
								output_scores.update({F'''{name}/recall''': recall, F'''{name}/precision''': precision, F'''{name}/f1''': fa}    )

								logger.info(
								    name.ljust(10    )  ,     F'''Recall: {recall * 1_00:.2f}'''  ,     F''' Precision: {precision * 1_00:.2f}'''  ,     F''' F1: {fa * 1_00:.2f}'''  ,     )

				if conll_subparts_num == 3:
								UpperCamelCase_:				Optional[int]						=    (conll / 3) * 1_00
								logger.info(F'''CoNLL score: {conll:.2f}'''    )
								output_scores.update({"""conll_score""": conll}    )

				return output_scores







def 							A__       (    lowerCamelCase    )	->						Tuple:
				UpperCamelCase_:				Union[str, Any]						=    False
				for line in key_lines:
								if not line.startswith("""#"""    ):
												if len(line.split()    ) > 6:
																UpperCamelCase_:				Any						=    line.split()[5]
																if not parse_col == "-":
																				UpperCamelCase_:				Optional[Any]						=    True
																				break
																else:
																				break
				return has_gold_parse





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,							_KWARGS_DESCRIPTION	)
class 							_UpperCamelCase					(       datasets.Metric	):






			'''simple docstring'''

			def    lowerCAmelCase__						(							self			:							Tuple   ):
							return datasets.MetricInfo(
							    description=_DESCRIPTION    ,						citation=_CITATION    ,						inputs_description=_KWARGS_DESCRIPTION    ,						features=datasets.Features(
							        {
							            """predictions""": datasets.Sequence(datasets.Value("""string"""   )   ),
							            """references""": datasets.Sequence(datasets.Value("""string"""   )   ),
							        }   )    ,						codebase_urls=["""https://github.com/ns-moosavi/coval"""]    ,						reference_urls=[
							        """https://github.com/ns-moosavi/coval""",
							        """https://www.aclweb.org/anthology/P16-1060""",
							        """http://www.conll.cemantix.org/2012/data.html""",
							    ]    ,						)




			def    lowerCAmelCase__						(							self			:							List[Any]    ,						snake_case_			:							int    ,						snake_case_			:							str    ,						snake_case_			:							List[str]=True    ,						snake_case_			:							Dict=False    ,						snake_case_			:							int=False    ,						snake_case_			:							Optional[Any]=False   ):
							UpperCamelCase_:				Any						=    [
							    ("""mentions""", evaluator.mentions),
							    ("""muc""", evaluator.muc),
							    ("""bcub""", evaluator.b_cubed),
							    ("""ceafe""", evaluator.ceafe),
							    ("""lea""", evaluator.lea),
							]

							if min_span:
											UpperCamelCase_:				Union[str, Any]						=    util.check_gold_parse_annotation(snake_case_   )
											if not has_gold_parse:
															raise NotImplementedError("""References should have gold parse annotation to use 'min_span'."""   )
															# util.parse_key_file(key_file)
															# key_file = key_file + ".parsed"

							UpperCamelCase_:				Optional[Any]						=    evaluate(
							    key_lines=snake_case_    ,						sys_lines=snake_case_    ,						metrics=snake_case_    ,						NP_only=snake_case_    ,						remove_nested=snake_case_    ,						keep_singletons=snake_case_    ,						min_span=snake_case_    ,						)

							return score








import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCamelCase_    :			int    =	16
lowerCamelCase_    :			str    =	32







def 							A__       (    lowerCamelCase    )	->						int:
				return int(x / 2**20    )

class 							_UpperCamelCase					:






			'''simple docstring'''

			def __enter__(							self			:							int   ):
							gc.collect()
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
							UpperCamelCase_:				str						=    torch.cuda.memory_allocated()
							return self




			def __exit__(							self			:							List[Any]    ,						*snake_case_			:							Union[str, Any]   ):
							gc.collect()
							torch.cuda.empty_cache()
							UpperCamelCase_:				List[str]						=    torch.cuda.memory_allocated()
							UpperCamelCase_:				int						=    torch.cuda.max_memory_allocated()
							UpperCamelCase_:				Optional[int]						=    bamb(self.end - self.begin   )
							UpperCamelCase_:				Tuple						=    bamb(self.peak - self.begin   )
							# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")







def 							A__       (    lowerCamelCase  ,     lowerCamelCase = 16  ,     lowerCamelCase = "bert-base-cased"  ,     lowerCamelCase = 3_20  ,     lowerCamelCase = 1_60  ,     )	->						Dict:
				UpperCamelCase_:				str						=    AutoTokenizer.from_pretrained(lowerCamelCase    )
				UpperCamelCase_:				List[str]						=    load_dataset(
				    """glue"""  ,     """mrpc"""  ,     split={"""train""": F'''train[:{n_train}]''', """validation""": F'''validation[:{n_val}]'''}    )

				def tokenize_function(lowerCamelCase    ):
								# max_length=None => use the model max length (it's actually the default)
								UpperCamelCase_:				Optional[Any]						=    tokenizer(examples["""sentence1"""]  ,     examples["""sentence2"""]  ,     truncation=lowerCamelCase  ,     max_length=lowerCamelCase    )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				UpperCamelCase_:				Dict						=    datasets.map(
				    lowerCamelCase  ,     batched=lowerCamelCase  ,     remove_columns=["""idx""", """sentence1""", """sentence2"""]  ,     load_from_cache_file=lowerCamelCase    )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				UpperCamelCase_:				int						=    tokenized_datasets.rename_column("""label"""  ,     """labels"""    )

				def collate_fn(lowerCamelCase    ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(lowerCamelCase  ,     padding="""max_length"""  ,     max_length=1_28  ,     return_tensors="""pt"""    )
								return tokenizer.pad(lowerCamelCase  ,     padding="""longest"""  ,     return_tensors="""pt"""    )

				# Instantiate dataloaders.
				UpperCamelCase_:				List[str]						=    DataLoader(
				    tokenized_datasets["""train"""]  ,     shuffle=lowerCamelCase  ,     collate_fn=lowerCamelCase  ,     batch_size=lowerCamelCase    )
				UpperCamelCase_:				Optional[Any]						=    DataLoader(
				    tokenized_datasets["""validation"""]  ,     shuffle=lowerCamelCase  ,     collate_fn=lowerCamelCase  ,     batch_size=lowerCamelCase    )

				return train_dataloader, eval_dataloader







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Union[str, Any]:
				# Initialize accelerator
				UpperCamelCase_:				Any						=    Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				UpperCamelCase_:				str						=    config["""lr"""]
				UpperCamelCase_:				Any						=    int(config["""num_epochs"""]    )
				UpperCamelCase_:				Tuple						=    int(config["""seed"""]    )
				UpperCamelCase_:				Dict						=    int(config["""batch_size"""]    )
				UpperCamelCase_:				Dict						=    args.model_name_or_path

				set_seed(lowerCamelCase    )
				UpperCamelCase_, UpperCamelCase_:				int						=    get_dataloaders(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     args.n_train  ,     args.n_val    )

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				UpperCamelCase_:				Any						=    AutoModelForSequenceClassification.from_pretrained(lowerCamelCase  ,     return_dict=lowerCamelCase    )

				# Instantiate optimizer
				UpperCamelCase_:				Tuple						=    (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				UpperCamelCase_:				List[Any]						=    optimizer_cls(params=model.parameters()  ,     lr=lowerCamelCase    )

				if accelerator.state.deepspeed_plugin is not None:
								UpperCamelCase_:				Any						=    accelerator.state.deepspeed_plugin.deepspeed_config[
								    """gradient_accumulation_steps"""
								]
				else:
								UpperCamelCase_:				List[Any]						=    1
				UpperCamelCase_:				Optional[int]						=    (len(lowerCamelCase    ) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								UpperCamelCase_:				List[Any]						=    get_linear_schedule_with_warmup(
								    optimizer=lowerCamelCase  ,     num_warmup_steps=0  ,     num_training_steps=lowerCamelCase  ,     )
				else:
								UpperCamelCase_:				Dict						=    DummyScheduler(lowerCamelCase  ,     total_num_steps=lowerCamelCase  ,     warmup_num_steps=0    )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				str						=    accelerator.prepare(
				    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )

				# We need to keep track of how many total steps we have iterated over
				UpperCamelCase_:				str						=    0
				# We also need to keep track of the stating epoch so files are named properly
				UpperCamelCase_:				Dict						=    0

				# Now we train the model
				UpperCamelCase_:				Dict						=    {}
				for epoch in range(lowerCamelCase  ,     lowerCamelCase    ):
								with TorchTracemalloc() as tracemalloc:
												model.train()
												for step, batch in enumerate(lowerCamelCase    ):
																UpperCamelCase_:				Union[str, Any]						=    model(**lowerCamelCase    )
																UpperCamelCase_:				str						=    outputs.loss
																UpperCamelCase_:				List[str]						=    loss / gradient_accumulation_steps
																accelerator.backward(lowerCamelCase    )
																if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

																overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
								accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin    )    )    )
								accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used    )    )
								accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked    )    )
								accelerator.print(
								    """Total Peak Memory consumed during the train (max): {}""".format(
								        tracemalloc.peaked + bamb(tracemalloc.begin    )    )    )
								UpperCamelCase_:				Optional[Any]						=    tracemalloc.peaked + bamb(tracemalloc.begin    )
								if args.peak_memory_upper_bound is not None:
												assert (
												    train_total_peak_memory[F'''epoch-{epoch}'''] <= args.peak_memory_upper_bound
												), "Peak memory usage exceeded the upper bound"

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
								with open(os.path.join(args.output_dir  ,     """peak_memory_utilization.json"""    )  ,     """w"""    ) as f:
												json.dump(lowerCamelCase  ,     lowerCamelCase    )







def 							A__       (    )	->						Optional[Any]:
				UpperCamelCase_:				Any						=    argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""    )
				parser.add_argument(
				    """--model_name_or_path"""  ,     type=lowerCamelCase  ,     default="""bert-base-cased"""  ,     help="""Path to pretrained model or model identifier from huggingface.co/models."""  ,     required=lowerCamelCase  ,     )
				parser.add_argument(
				    """--output_dir"""  ,     type=lowerCamelCase  ,     default="""."""  ,     help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""  ,     )
				parser.add_argument(
				    """--peak_memory_upper_bound"""  ,     type=lowerCamelCase  ,     default=lowerCamelCase  ,     help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."""  ,     )
				parser.add_argument(
				    """--n_train"""  ,     type=lowerCamelCase  ,     default=3_20  ,     help="""Number of training examples to use."""  ,     )
				parser.add_argument(
				    """--n_val"""  ,     type=lowerCamelCase  ,     default=1_60  ,     help="""Number of validation examples to use."""  ,     )
				parser.add_argument(
				    """--num_epochs"""  ,     type=lowerCamelCase  ,     default=1  ,     help="""Number of train epochs."""  ,     )
				UpperCamelCase_:				Optional[int]						=    parser.parse_args()
				UpperCamelCase_:				Union[str, Any]						=    {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
				training_function(lowerCamelCase  ,     lowerCamelCase    )


if __name__ == "__main__":
							main()






